def test_pixel_stdev(self):
"""Test that sources are detected on a simulated image with
thresholdType='pixel_stdev', and that they have the right significance.
"""
exposure, schema, numX, numY, starSigma = self._create_exposure()
config = SourceDetectionTask.ConfigClass()
config.thresholdType = "pixel_stdev"
config.reEstimateBackground = False
# TempLocalBackground changes the peak value of the faintest peak,
# so disable it for this test so that we can calculate an expected
# answer without having to try to deal with backgrounds.
config.doTempLocalBackground = False
task = SourceDetectionTask(config=config, schema=schema)
# Don't smooth, so that we can directly calculate the s/n from the exposure.
result = task.detectFootprints(exposure, doSmooth=False)
self.assertEqual(result.numPos, numX * numY)
self.assertEqual(result.numNeg, 0)
# Significance values for `pixel_stdev` should match image/sqrt(variance).
for footprint in result.positive.getFootprints():
for peak in footprint.peaks:
point = lsst.geom.Point2I(peak.getIx(), peak.getIy())
value = exposure.image[point]
stddev = np.sqrt(exposure.variance[point])
with self.subTest(str(point)):
self.assertFloatsAlmostEqual(peak["significance"],
value / stddev,
rtol=1e-7,
msg=str(point))
def test_stdev(self):
"""Test that sources are detected on a simulated image with
thresholdType='stdev'.
"""
exposure, schema, numX, numY, starSigma = self._create_exposure()
config = SourceDetectionTask.ConfigClass()
# don't modify the image after detection.
config.reEstimateBackground = False
config.thresholdType = "stdev"
task = SourceDetectionTask(config=config, schema=schema)
self._check_detectFootprints(exposure,
numX,
numY,
starSigma,
task,
config,
doSmooth=True)
self._check_detectFootprints(exposure,
numX,
numY,
starSigma,
task,
config,
doSmooth=False)
def test_significance(self):
"""Test that negative peaks have the right significance for
thresholdType='stdev' for the non-convolved, non-local-background case.
"""
exposure, numX, numY = self._create_exposure()
schema = afwTable.SourceTable.makeMinimalSchema()
config = SourceDetectionTask.ConfigClass()
config.thresholdPolarity = 'both'
# don't modify the image after detection.
config.reEstimateBackground = False
config.doTempLocalBackground = False
detection = SourceDetectionTask(config=config, schema=schema)
result = detection.detectFootprints(exposure, doSmooth=False)
bad = exposure.mask.getPlaneBitMask(config.statsMask)
sctrl = afwMath.StatisticsControl()
sctrl.setAndMask(bad)
stats = afwMath.makeStatistics(exposure.maskedImage, afwMath.STDEVCLIP,
sctrl)
stddev = stats.getValue(afwMath.STDEVCLIP)
# Don't bother checking positive footprints: those are tested more
# thoroughly in test_detection.py.
for footprint in result.negative.getFootprints():
for peak in footprint.peaks:
point = lsst.geom.Point2I(peak.getIx(), peak.getIy())
value = exposure.image[point]
with self.subTest(str(point)):
self.assertFloatsAlmostEqual(
peak["significance"],
-value / stddev, # S/N for negative peak
rtol=1e-7,
msg=str(point))
def test_detection_stdev(self):
"""Test detection and measurement on an exposure with negative sources
for thresholdType="stdev".
"""
exposure, numX, numY = self._create_exposure()
if display:
disp = afwDisplay.Display(frame=1)
disp.mtv(exposure,
title=self._testMethodName + ": image with -ve sources")
schema = afwTable.SourceTable.makeMinimalSchema()
config = SourceDetectionTask.ConfigClass()
config.reEstimateBackground = False
config.thresholdPolarity = 'both'
detection = SourceDetectionTask(config=config, schema=schema)
algMetadata = dafBase.PropertyList()
measurement = SourceMeasurementTask(schema=schema,
algMetadata=algMetadata)
table = afwTable.SourceTable.make(schema)
detections = detection.run(table, exposure)
sources = detections.sources
fpSets = detections.fpSets
self.assertEqual(len(sources), numX * numY)
self.assertEqual(fpSets.numPos, numX * numY / 2)
self.assertEqual(fpSets.numNeg, numX * numY / 2)
measurement.run(sources, exposure)
nGoodCent = 0
nGoodShape = 0
for s in sources:
cent = s.getCentroid()
shape = s.getShape()
if cent[0] == cent[0] and cent[1] == cent[1]:
nGoodCent += 1
if (shape.getIxx() == shape.getIxx()
and shape.getIyy() == shape.getIyy()
and shape.getIxy() == shape.getIxy()):
nGoodShape += 1
if display:
xy = cent[0], cent[1]
disp.dot('+', *xy)
disp.dot(shape, *xy, ctype=afwDisplay.RED)
self.assertEqual(nGoodCent, numX * numY)
self.assertEqual(nGoodShape, numX * numY)
def testBasics(self):
bbox = afwGeom.Box2I(afwGeom.Point2I(256, 100),
afwGeom.Extent2I(128, 127))
minCounts = 5000
maxCounts = 50000
starSigma = 1.5
numX = 5
numY = 5
coordList = self.makeCoordList(
bbox=bbox,
numX=numX,
numY=numY,
minCounts=minCounts,
maxCounts=maxCounts,
sigma=starSigma,
)
kwid = 11
sky = 2000
addPoissonNoise = True
exposure = plantSources(bbox=bbox,
kwid=kwid,
sky=sky,
coordList=coordList,
addPoissonNoise=addPoissonNoise)
schema = afwTable.SourceTable.makeMinimalSchema()
config = SourceDetectionTask.ConfigClass()
config.reEstimateBackground = False
task = SourceDetectionTask(config=config, schema=schema)
for doSmooth in (False, True):
taskSigma = 2.2
res = task.detectFootprints(exposure,
doSmooth=doSmooth,
sigma=taskSigma)
self.assertEqual(res.numPos, numX * numY)
self.assertEqual(res.numNeg, 0)
self.assertEqual(task.metadata.get("sigma"), taskSigma)
self.assertEqual(task.metadata.get("doSmooth"), doSmooth)
self.assertEqual(task.metadata.get("nGrow"),
int(taskSigma * config.nSigmaToGrow + 0.5))
res = task.detectFootprints(exposure,
doSmooth=doSmooth,
sigma=None)
taskSigma = task.metadata.get("sigma")
self.assertTrue(abs(taskSigma - starSigma) < 0.1)
self.assertEqual(res.numPos, numX * numY)
self.assertEqual(res.numNeg, 0)
def checkExposure(original, doTempLocalBackground,
doTempWideBackground):
config = SourceDetectionTask.ConfigClass()
config.reEstimateBackground = False
config.thresholdType = "pixel_stdev"
config.doTempLocalBackground = doTempLocalBackground
config.doTempWideBackground = doTempWideBackground
schema = afwTable.SourceTable.makeMinimalSchema()
task = SourceDetectionTask(config=config, schema=schema)
exposure = original.clone()
task.detectFootprints(exposure, sigma=3.21)
self.assertFloatsEqual(exposure.image.array, original.image.array)
# Mask is permitted to vary: DETECTED bit gets set
self.assertFloatsEqual(exposure.variance.array,
original.variance.array)
def test_significance_stdev(self):
"""Check the non-smoothed, non-background updated peak significance
values with thresholdType="stddev".
"""
exposure, schema, numX, numY, starSigma = self._create_exposure()
config = SourceDetectionTask.ConfigClass()
# don't modify the image after detection.
config.reEstimateBackground = False
config.doTempLocalBackground = False
config.thresholdType = "stdev"
task = SourceDetectionTask(config=config, schema=schema)
result = self._check_detectFootprints(exposure,
numX,
numY,
starSigma,
task,
config,
doSmooth=False)
bad = exposure.mask.getPlaneBitMask(config.statsMask)
sctrl = afwMath.StatisticsControl()
sctrl.setAndMask(bad)
stats = afwMath.makeStatistics(exposure.maskedImage, afwMath.STDEVCLIP,
sctrl)
stddev = stats.getValue(afwMath.STDEVCLIP)
for footprint in result.positive.getFootprints():
for peak in footprint.peaks:
point = lsst.geom.Point2I(peak.getIx(), peak.getIy())
value = exposure.image[point]
with self.subTest(str(point)):
self.assertFloatsAlmostEqual(peak["significance"],
value / stddev,
rtol=1e-7,
msg=str(point))
def testBasics(self):
bbox = afwGeom.Box2I(afwGeom.Point2I(256, 100),
afwGeom.Extent2I(128, 127))
minCounts = 2000
maxCounts = 20000
starSigma = 1.5
numX = 4
numY = 4
coordList = self.makeCoordList(
bbox=bbox,
numX=numX,
numY=numY,
minCounts=minCounts,
maxCounts=maxCounts,
sigma=starSigma,
)
kwid = 11
sky = 2000
addPoissonNoise = True
exposure = plantSources(bbox=bbox,
kwid=kwid,
sky=sky,
coordList=coordList,
addPoissonNoise=addPoissonNoise)
if display:
ds9.mtv(exposure)
schema = afwTable.SourceTable.makeMinimalSchema()
config = SourceDetectionTask.ConfigClass()
config.reEstimateBackground = False
config.thresholdPolarity = 'both'
detection = SourceDetectionTask(config=config, schema=schema)
algMetadata = dafBase.PropertyList()
measurement = SourceMeasurementTask(schema=schema,
algMetadata=algMetadata)
table = afwTable.SourceTable.make(schema)
detections = detection.makeSourceCatalog(table, exposure)
sources = detections.sources
fpSets = detections.fpSets
self.assertEqual(len(sources), numX * numY)
self.assertEqual(fpSets.numPos, numX * numY / 2)
self.assertEqual(fpSets.numNeg, numX * numY / 2)
measurement.run(sources, exposure)
nGoodCent = 0
nGoodShape = 0
for s in sources:
cent = s.getCentroid()
shape = s.getShape()
if cent[0] == cent[0] and cent[1] == cent[1]:
nGoodCent += 1
if (shape.getIxx() == shape.getIxx()
and shape.getIyy() == shape.getIyy()
and shape.getIxy() == shape.getIxy()):
nGoodShape += 1
if display:
xy = cent[0] - exposure.getX0(), cent[1] - exposure.getY0()
ds9.dot('+', *xy)
ds9.dot(shape, *xy, ctype=ds9.RED)
self.assertEqual(nGoodCent, numX * numY)
self.assertEqual(nGoodShape, numX * numY)
subConfig[
algName].maxSincRadius = 0 # Disable sinc photometry because we're undersampled
for subConfig in (measure_config.plugins, measure_config.undeblended):
subConfig.names.add(algName2)
values = [ii / 0.168 for ii in (0.65, 0.85, 1.1)]
algConfig = subConfig[algName2]
algConfig.seeing = values
algConfig.aperture.radii = [3, 6, 12, 24]
algConfig.aperture.maxSincRadius = 0
measure_config.load(
'/tigress/rea3/lsst/DM-8059/obs_subaru/config/apertures.py')
deblend_config = SourceDeblendTask.ConfigClass()
detect_config = SourceDetectionTask.ConfigClass()
detect_config.isotropicGrow = True
detect_config.doTempLocalBackground = False
detect_config.thresholdValue = 5
detect_config.nSigmaToGrow = args.grow
schema = afwTable.SourceTable.makeMinimalSchema()
detectTask = SourceDetectionTask(schema, config=detect_config)
deblendTask = SourceDeblendTask(schema, config=deblend_config)
measureTask = SingleFrameMeasurementTask(schema, config=measure_config)
peakMinimalSchema = afwDet.PeakTable.makeMinimalSchema()
peakSchemaMapper = afwTable.SchemaMapper(peakMinimalSchema, schema)
truthKey = schema.addField('truth_index',
type=int,
